An enhanced multi-view human action recognition system for virtual training simulator

Beom Kwon; Junghwan Kim; Sanghoon Lee

doi:10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator

Beom Kwon, Junghwan Kim, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.

Original language	English
Title of host publication	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9789881476821
DOIs	https://doi.org/10.1109/APSIPA.2016.7820895
Publication status	Published - 2017 Jan 17
Event	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16

Publication series

Name	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country	Korea, Republic of
City	Jeju
Period	16/12/13 → 16/12/16

Bibliographical note

Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2016.7820895

Fingerprint Dive into the research topics of 'An enhanced multi-view human action recognition system for virtual training simulator'. Together they form a unique fingerprint.

Cite this

Kwon, B., Kim, J., & Lee, S. (2017). An enhanced multi-view human action recognition system for virtual training simulator. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820895] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820895

Kwon, Beom ; Kim, Junghwan ; Lee, Sanghoon. / An enhanced multi-view human action recognition system for virtual training simulator. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

@inproceedings{8e44d208c8724ad9b8a3deddf4cacc08,

title = "An enhanced multi-view human action recognition system for virtual training simulator",

abstract = "Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.",

author = "Beom Kwon and Junghwan Kim and Sanghoon Lee",

note = "Publisher Copyright: {\textcopyright} 2016 Asia Pacific Signal and Information Processing Association.; 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 ; Conference date: 13-12-2016 Through 16-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/APSIPA.2016.7820895",

language = "English",

series = "2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016",

address = "United States",

}

Kwon, B, Kim, J & Lee, S 2017, An enhanced multi-view human action recognition system for virtual training simulator. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820895, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator. / Kwon, Beom; Kim, Junghwan; Lee, Sanghoon.

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820895 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An enhanced multi-view human action recognition system for virtual training simulator

AU - Kwon, Beom

AU - Kim, Junghwan

AU - Lee, Sanghoon

PY - 2017/1/17

Y1 - 2017/1/17

N2 - Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.

AB - Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.

UR - http://www.scopus.com/inward/record.url?scp=85013844705&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013844705&partnerID=8YFLogxK

U2 - 10.1109/APSIPA.2016.7820895

DO - 10.1109/APSIPA.2016.7820895

M3 - Conference contribution

AN - SCOPUS:85013844705

T3 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Y2 - 13 December 2016 through 16 December 2016

ER -

Kwon B, Kim J, Lee S. An enhanced multi-view human action recognition system for virtual training simulator. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820895. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). https://doi.org/10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Cite this